relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3168/
title: Cow Behavior Monitoring Using a Multidimensional   Acceleration Sensor and Multiclass SVM
creator: Hoang, Quang Trung
creator: Phung, Cong Phi Khanh
creator: Bui, Trung Ninh
creator: Chu, Thi Phuong Dung
creator: Tran, Duc Tan
subject: Electronics and Computer Engineering
description: The daily behavior of dairy cows reflects the health status and well being.An automated monitoring system is needed for suitable management. It helps farmers to have a comprehensive view of the cattle healthy and manage large of cows. Acceleration sensors can be found in various kinds of applications. In this paper, we detect the cow’s activities by using a multidimensional acceleration sensor and multiclass support vector machine (SVM). The acceleration sensor is attached to the cow’s neck-collar in order to sense the movements in X, Y, and Z axes. The data is brought to a microprocessor for pre-processing, and join in a wireless sensor network (WSN) through a Zigbee module. After that, the data are transferred to the server. At the server, a suitable SVM algorithm is chosen and applied to classify four main behaviors: standing, lying, feeding and walking. A well know kernels, Radius   Basic Function (RBF), is chosen. After that, a cross validation (k-fold) is used to measure the error and select the best fit model. The sensor is used to acquire experimental data from Vietnam Yellow cows in the cattle   farm. The promising results with the average sensitivity of 87.51% and the average precision of 90.24% confirm the reliability of our solution. The classification results can be automatically uploaded to the cloud internet and the farmer can easily access to check the status of his cows.
date: 2018
type: Article
type: PeerReviewed
identifier:   Hoang, Quang Trung and Phung, Cong Phi Khanh and Bui, Trung Ninh and Chu, Thi Phuong Dung and Tran, Duc Tan  (2018) Cow Behavior Monitoring Using a Multidimensional Acceleration Sensor and Multiclass SVM.  International Journal of Machine Learning and Networked Collaborative Engineering, 2  (3).   pp. 110-118.  ISSN 2581-3242